The recommendation algorithm based on knowledge graph is mainly considered from the following three aspects: a) Method based on graph neural network. It captures the high-level structure in graph and refines the embedding of users and items. For example, RippleNet [1]} spreads users' potential preferences in KG and explores their hierarchical interests. Wang et al. [2]} use a KG graph convolutional network (GCN [3]}), which is incorporated to generate high-level term connectivity features. KGAT [4]} explicitly models the high-level connections by an end-to-end manner. b) Method based on embedding, which combines the entities and relationships of KG into a continuous vector space, and helps recommendation system by enhancing semantic representation. For example, DKN [5]} combines the semantic and knowledge-level representations of news, and incorporates KG representation into news recommendations. In addition, CKE [6]} combines CF module with structure, text and visual knowledge into a unified recommendation framework. c) Method based on path. It uses paths and related user-item pairs to explore the connection mode between items in KG. For example, MCRec [7]} learns the explicit representation of meta-paths in recommendation. In addition, it also considers meta-paths and the interactions between users and items. Compared with the method based on embedding, the method based on path develops KG structure more naturally and intuitively. RKGE [8]} and KPRN [9]} that needn't to design meta-paths automatically collect the paths between users and items, and use Recurrent Neural Network (RNN) to encode the paths.
